计算机工程2018，Vol.44Issue(1)：35-43,9.DOI:10.3969/j.issn.1000-3428.2018.01.006
基于联合非负矩阵分解的话题变迁检测方法
Topic Change Detection Method Based on Joint Nonnegative Matrix Factorization
摘要
Abstract
In large-scale temporal documents similarities and differences do not have the ability to identily topics from temporal documents and to track and analyze topics over time.To this end,a method of topic change detection for temporal document corpus is proposed.Similar topics and similarities and foundations are found in the temporal document corpus.Using the improved joint Nonnegative Matrix Factorization (NMF) algorithm,similarities and differences were found in the the timeseries document.To avoid the introduction of noise topics,by calculating the topic of all topic entropy,in order to obtain high-quality topics.Use the word cloud and trend graph to analyze the trend of topic change.Experimental results of two real data sets,20Newsgroups and LTN2011 show that this method can effectively find similarities and differences from the tempord of documents,and the extraction topic is effect and the accuracy is high.关键词
联合非负矩阵分解/话题模型/时序异同话题/优质话题/话题变迁检测Key words
Joint Nonnegative Matrix Factorization (NMF)/topic model/temporal similarities and differences topic/high quality topic/topic change detection分类
信息技术与安全科学引用本文复制引用
陈梦伟,吕钊,崔修涛..基于联合非负矩阵分解的话题变迁检测方法[J].计算机工程,2018,44(1):35-43,9.基金项目
上海市科学技术委员会科研计划项目(16511102702) （16511102702）
上海市经济和信息化委员会项目(150643). （150643）
